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Job Research

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low-hands
job-research

Research public company, product-line, business, market, and competitor context for one saved job when the user explicitly requests it. Do not use automatically during job matching, resume tailoring, or interview preparation, and do not claim to recover a specific team's private work from a generic JD.

Overview

Publisherlow-hands
RepositoryMyCareer
Skill namejob-research
Stars
102
Forks
0
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by low-hands on GitHub. Read the source before you install it.

Installation

Install the Job Research AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/low-hands/MyCareer/tree/main/skills/job-research
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/low-hands/MyCareer.git /tmp/MyCareer
mkdir -p .claude/skills
cp -r /tmp/MyCareer/skills/job-research .claude/skills/job-research
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Job Research in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Job Research on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Job Research is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Job Research

Research public context that helps the user understand a saved job. Treat the exact JD as a source of search anchors, not proof that public company material describes this specific role or team. External sources add context but must not silently rewrite or strengthen the JD.

This is an optional, user-requested activity. Its existence does not make it a required step in job matching, resume tailoring, application tracking, interview preparation, or mock interviews.

The research scope may contain user_provided_context, such as product or business clues the user reports hearing in an interview and explicitly agrees to use for research. Treat it only as an unverified search lead. It is not a public source, cannot support a fact, and cannot become confirmed merely because a search result contains similar words. Do not expose private names, contact details, or unrelated interview recollections in queries or findings.

Research priorities

Spend limited source capacity in this order:

  1. official material about a product or business line explicitly named in the JD or supplied by the user;
  2. current primary product, business, investor, regulatory, or technical material about that public context;
  3. reputable secondary reporting about relevant market changes or competitors;
  4. company-level background only when no narrower public anchor exists or it materially helps the user's stated question.

Search for products, customers, business models, public initiatives, market conditions, competitors, and technical context when the JD or user request gives a defensible link. Do not spend sources on generic history, slogans, rankings, or interview-question lists unless the requested focus makes them directly useful.

If the JD contains no product-line or business anchor, do not manufacture one. Limit the result to the company-level question the user actually asked, state that its relationship to the role cannot be confirmed, and put the missing role-specific context in open_questions or limitations.

Chinese-market sources

Generic web search underserves Chinese employers: the useful primary material is concentrated in places a broad query rarely surfaces. When the company is Chinese, spend source capacity on these before falling back to general results.

QuestionWhere the primary material is
Products, architecture, technical directionthe company's own engineering blog, its open-source repositories, conference talks
Business lines, funding, ownership, scale招股书, 年报, 投资方公告, 工商信息 (天眼查, 企查查)
Market position, competitors, recent moves36氪, 晚点LatePost, 界面新闻, 第一财经, and the company's own announcements
Regulatory or licensing constraintsthe issuing body's own publication

Prefer the company's own material and the issuing body's own publication over reporting about them. Chinese tech media frequently republishes the same wire copy; three outlets carrying one story are one source, not three, and citing them as three overstates the support behind a finding.

Self-reported content is not a primary source

Anonymous or pseudonymous posts on career and social platforms — 脉脉, 牛客网, 知乎, 小红书, CSDN, and similar — are individual recollections. They may not support a fact under any circumstances. At most they support an inference with low confidence, phrased as what some people report rather than as what is the case, and only when several independent posts agree and nothing primary contradicts them.

What belongs to interview preparation instead

Compensation bands, interview loops, question banks, and hiring-process timelines are out of scope here even when such posts are easy to find. Job research answers what the company does and where it stands; the material a candidate needs to walk into a specific interview is a different request with a different consumer. Do not widen a company-research run into it, and do not report those numbers as company facts.

Evidence boundaries

  • A fact states only what cited sources support.
  • An inference connects cited evidence to the role and must be phrased as a possibility or reasoned interpretation, not employer-confirmed truth.
  • An unknown records a material unanswered question and cites no source as if absence proved the answer.
  • Company-wide information does not prove that a particular role or team works on that product, follows the same process, uses the same stack, operates at the same scale, or shares the same culture.
  • Do not search for or infer private team roadmaps, reporting lines, headcount, staffing reasons, current projects, or day-to-day responsibilities.
  • Never claim likely interview questions, hiring stages, headcount, internal plans, compensation, or team practices without suitable current evidence.
  • Conflicting sources remain a limitation; do not average them into a false fact.

Sources

Use only sources actually returned by web research in this run. Give every source a short stable key such as S1, preserve its canonical public URL, title, publisher when known, publication date when supported, and a short relevant excerpt. The excerpt is evidence, not a place to copy an article.

Every fact and inference must cite at least one returned source key. Do not include unused sources, duplicate URLs, search-result pages, inaccessible URLs, or a source that merely mentions the company without supporting a finding.

Output quality

Focus the summary on the public company, product, and business context the user asked for. Keep findings individually testable, separate fact from inference, surface unanswered questions, and state limitations caused by stale, missing, conflicting, or company-wide-only evidence. Return only the configured structured response.

Frequently asked questions

What does the Job Research AI skill do?

Research public company, product-line, business, market, and competitor context for one saved job when the user explicitly requests it. Do not use automatically during job matching, resume tailoring, or interview preparation, and do not claim to recover a specific team's private work from a generic JD.

Why use Job Research on TypingMind?

Because you install it once and use it with any model. Job Research is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Job Research in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/low-hands/MyCareer/tree/main/skills/job-research. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Job Research?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Job Research?

As many as you like. As long as a model supports skills, you can use Job Research with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Job Research AI skill free?

Yes. It is published on GitHub by low-hands under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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